We created a pool of self-supervised speech representations by retrieving official models using S3PRL [1]} https://github.com/s3prl/s3prl and Huggingface. https://huggingface.co/ To give a fair comparison, we attempted to constrain the scope of analysis to the models pre-trained on LibriSpeech 960 hours corpus [2]} or LibriLight 60K hours corpus [3]}, and we exclude some extension methods or variants. Although WavLM [4]} is an extension of HuBERT [5]}, the large version of the model is included as a reference of the best score we can get from SSL models. As a supervised counterpart, the encoder of Fairseq S2T [6]} is added, which was trained for ASR using LibriSpeech. Finally, some handcrafted acoustic features are included as the baselines. The final list includes: (contrastive model) Wav2Vec [7]}, VQ-Wav2Vec [8]} and Wav2Vec 2.0 [9]}, (predictive model) HuBERT, Data2Vec [10]} and WavLM, (generative model) TERA [11]} and DeCoAR 2.0 [12]}, (supervised model) Fairseq S2T, and (hand crafted) filter bank, mel-spectrogram and MFCC (Fig. REF ).
